An Efficient FAKE NEWS DETECTOR

Bhoir, Smita

doi:10.1109/iccci48352.2020.9104177

Cited by 7 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our research applied NB, RF, and SVM classifiers. The selection of these machine learning algorithms is due to their performance identifying fake news in similar models (NB [26], RF [64], SVM [65]), and are considered adequate in many classification applications. NB classification technique is based on Bayes' Theorem with an assumption of independence among deception features, which holds for our 31 features.…”

Section: Training Of Deception Detectionmentioning

confidence: 99%

Arabic Fake News Detection Based on Textual Analysis

et al. 2022

View full text Add to dashboard Cite

Over the years, social media has had a considerable impact on the way we share information and send messages. With this comes the problem of the rapid distribution of fake news which can have negative impacts on both individuals and society. Given the potential negative influence, detecting unmonitored ‘fake news’ has become a critical issue in mainstream media. While there are recent studies that built machine learning models that detect fake news in several languages, lack of studies in detecting fake news in the Arabic language is scare. Hence, in this paper, we study the issue of fake news detection in the Arabic language based on textual analysis. In an attempt to address the challenges of authenticating news, we introduce a supervised machine learning model that classifies Arabic news articles based on their context’s credibility. We also introduce the first dataset of Arabic fake news articles composed through crowdsourcing. Subsequently, to extract textual features from the articles, we create a unique approach of forming Arabic lexical wordlists and design an Arabic Natural Language Processing tool to perform textual features extraction. The findings of this study promises great results and outperformed human performance in the same task.

show abstract

Section: Training Of Deception Detectionmentioning

confidence: 99%